Region-Wide Comprehensive Implementation of Roguing Infected Trees, Tree Replacement, and Insecticide Applications Successfully Controls Citrus Huanglongbing.

Huanglongbing (HLB) is a devastating citrus disease worldwide. A three-pronged approach to controlling HLB has been suggested, namely, removal of HLB-symptomatic trees, psyllid control, and replacement with HLB-free trees. However, such a strategy did not lead to successful HLB control in many citrus-producing regions, such as Florida. We hypothesize that this is because of the small-scale or incomprehensive implementation of the program; conversely, a comprehensive implementation of such a strategy at the regional level can successfully control HLB. To test our hypothesis, we investigated the effects of region-wide comprehensive implementation of this scheme to control HLB in Gannan region, China, with a total planted citrus acreage of over 110,000 ha from 2013 to 2019. With the region-wide implementation of comprehensive HLB management, the overall HLB incidence in Gannan decreased from 19.71% in 2014 to 3.86% in 2019. A partial implementation of such a program (without a comprehensive inoculum removal) at the regional level in Brazil resulted in HLB incidence increasing from 1.89% in 2010 to 19.02% in 2019. Using dynamic regression model analyses with data from both Brazil and China, we constructed a model to predict HLB incidence when all three components were applied at 100%. It was predicated that in a region-wide comprehensive implementation of such a program, HLB incidence would be controlled to a level of less than 1%. We conducted economic feasibility analyses and showed that average net profits were positive for groves that implemented the comprehensive strategy, but groves that did not implement it had negative net profits over a 10-year period. Overall, the key for the three-pronged program to successfully control HLB is the large scale (region-wide) and comprehensiveness in implementation. This study provides valuable information to control HLB and other economically important endemic diseases worldwide.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Unsupervised Learning of Depth and Camera Pose with Feature Map Warping.

Estimating the depth of image and egomotion of agent are important for autonomous and robot in understanding the surrounding environment and avoiding collision. Most existing unsupervised methods estimate depth and camera egomotion by minimizing photometric error between adjacent frames. However, the photometric consistency sometimes does not meet the real situation, such as brightness change, moving objects and occlusion. To reduce the influence of brightness change, we propose a feature pyramid matching loss (FPML) which captures the trainable feature error between a current and the adjacent frames and therefore it is more robust than photometric error. In addition, we propose the occlusion-aware mask (OAM) network which can indicate occlusion according to change of masks to improve estimation accuracy of depth and camera pose. The experimental results verify that the proposed unsupervised approach is highly competitive against the state-of-the-art methods, both qualitatively and quantitatively. Specifically, our method reduces absolute relative error (Abs Rel) by 0.017-0.088.

Bridging Visual and Textual Semantics: Towards Consistency for Unbiased Scene Graph Generation.

Scene Graph Generation (SGG) aims to detect visual relationships in an image. However, due to long-tailed bias, SGG is far from practical. Most methods depend heavily on the assistance of statistics co-occurrence to generate a balanced dataset, so they are dataset-specific and easily affected by noises. The fundamental cause is that SGG is simplified as a classification task instead of a reasoning task, thus the ability capturing the fine-grained details is limited and the difficulty in handling ambiguity is increased. By imitating the way of dual process in cognitive psychology, a Visual-Textual Semantics Consistency Network (VTSCN) is proposed to model the SGG task as a reasoning process, and relieve the long-tailed bias significantly. In VTSCN, as the rapid autonomous process (Type1 process), we design a Hybrid Union Representation (HUR) module, which is divided into two steps for spatial awareness and working memories modeling. In addition, as the higher order reasoning process (Type2 process), a Global Textual Semantics Modeling (GTS) module is designed to individually model the textual contexts with the word embeddings of pairwise objects. As the final associative process of cognition, a Heterogeneous Semantics Consistency (HSC) module is designed to balance the type1 process and the type2 process. Lastly, our VTSCN raises a new way for SGG model design by fully considering human cognitive process. Experiments on Visual Genome, GQA and PSG datasets show our method is superior to state-of-the-art methods, and ablation studies validate the effectiveness of our VTSCN.

Blockchain-Enabled Contextual Online Learning under Local Differential Privacy for Coronary Heart Disease Diagnosis in Mobile Edge Computing.

Due to the increasing medical data for coronary heart disease (CHD) diagnosis, how to assist doctors to make proper clinical diagnosis has attracted considerable attention. However, it faces many challenges, including personalized diagnosis, high dimensional datasets, clinical privacy concerns and insufficient computing resources. To handle these issues, we propose a novel blockchain-enabled contextual online learning model under local differential privacy for CHD diagnosis in mobile edge computing. Various edge nodes in the network can collaborate with each other to achieve information sharing, which guarantees that CHD diagnosis is suitable and reliable. To support the dynamically increasing dataset, we adopt a top-down tree structure to contain medical records which is partitioned adaptively. Furthermore, we consider patients' contexts (e.g., lifestyle, medical history records, and physical features) to provide more accurate diagnosis. Besides, to protect the privacy of patients and medical transactions without any trusted third party, we utilize the local differential privacy with randomised response mechanism and ensure blockchain-enabled information-sharing authentication under multi-party computation. Based on the theoretical analysis, we confirm that we provide real-time and precious CHD diagnosis for patients with sublinear regret, and achieve efficient privacy protection. The experimental results validate that our algorithm {outperforms} other algorithm benchmarks on running time, error rate and diagnosis accuracy.

V[Formula: see text]H: View Variation and View Heredity for Incomplete Multiview Clustering.

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V[Formula: see text]H). Inspired by the variation and the heredity in genetics, V[Formula: see text]H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V[Formula: see text]H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V[Formula: see text]H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V[Formula: see text]H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement-Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V[Formula: see text]H fully learns the information of each view. Compared with the state-of-the-art algorithms, V[Formula: see text]H improves clustering performance by more than 20% in representative cases. With the large improvement on multiple datasets, V[Formula: see text]H has wide potential applications including the analysis of pandemic, financial and election datasets. The DOI of our codes is 10.24 433/CO.2 119 636.v1.